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I am trying to use a model I was given by a colleague but keep coming up against an error finding base.PhotonBase.

Here is the code I ran:

from photonai.base import Hyperpipe
best_model_file = 'mymodel.photon'
my_model = Hyperpipe.load_optimum_pipe(best_model_file)

where mymodel.photon sits in a folder that also includes a "photon_best_model" folder with __optimum_pipe_0_SimpleImputer.pkl, _optimum_pipe_1_StandardScaler.pkl, _optimum_pipe_2_Ridge.pkl, and optimum_pipe_blueprint.pkl'
I requested these files from my colleague because the errors I was getting indicated they needed to be there to load the model.

Running this I get the following error:
YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details. defaults = yaml.load(f) /Users/lee_jollans/anaconda3/lib/python3.7/site-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+. warnings.warn(msg, category=FutureWarning) Could not load meta information for optimum pipe Traceback (most recent call last): File "tryphoton.py", line 10, in <module> my_model = Hyperpipe.load_optimum_pipe(best_model_file) File "/Users/lee_jollans/anaconda3/lib/python3.7/site-packages/photonai/base/hyperpipe.py", line 1105, in load_optimum_pipe return PhotonModelPersistor.load_optimum_pipe(file, password) File "/Users/lee_jollans/anaconda3/lib/python3.7/site-packages/photonai/base/hyperpipe.py", line 1444, in load_optimum_pipe element_list = PhotonModelPersistor.load_elements(folder=load_folder) File "/Users/lee_jollans/anaconda3/lib/python3.7/site-packages/photonai/base/hyperpipe.py", line 1410, in load_elements loaded_pipeline_element = joblib.load(os.path.join(folder, element_info['filename'] + '.pkl')) File "/Users/lee_jollans/anaconda3/lib/python3.7/site-packages/joblib/numpy_pickle.py", line 605, in load obj = _unpickle(fobj, filename, mmap_mode) File "/Users/lee_jollans/anaconda3/lib/python3.7/site-packages/joblib/numpy_pickle.py", line 529, in _unpickle obj = unpickler.load() File "/Users/lee_jollans/anaconda3/lib/python3.7/pickle.py", line 1085, in load dispatch[key[0]](self) File "/Users/lee_jollans/anaconda3/lib/python3.7/pickle.py", line 1373, in load_global klass = self.find_class(module, name) File "/Users/lee_jollans/anaconda3/lib/python3.7/pickle.py", line 1423, in find_class __import__(module, level=0) ModuleNotFoundError: No module named 'photonai.base.PhotonBase'

My colleague had originally noted that Hyperpipe is imported using from photonai.base.PhotonBase import Hyperpipe, which also did not work because I got the PhotonBase error.

Note, I am running macOS Mojave and python 3.7.3

Any help is greatly appreciated!

Hi,
I defined a classification hyperpipe that involves a PipelineElement that Oversamples or Undersamples the input dataset. I would like to know if this step is done only on the training split of the nested cross validation or also on the validation and test splits ? Actually, I would like to know if the metrics computed to select the best models and to evaluate them are only computed on the "real" samples and not on the "real + fake" ones (in case of an oversampling), and if it is computed on all the samples and not only the selected ones in case of an undersampling.
Do you know the answer or maybe a document where I can search for the answer ? I have not found it on the documentation but maybe I searched #badly.
Thanks a lot !
Clément

AttributeError: module 'sklearn.metrics._dist_metrics' has no attribute 'DatasetsPair' seems to occur when using imbalanced-learn>0.9.1. It took quite some time to find this "workaround" based on this as photonai asks for scikit-learn==1.1.3 and simply downgrading it to scikit-learn==1.1.0 (as suggested) did not work at all.

If cached SVC is used, the .json contains invalid entry ""__photon_type": "FileSystemStoreBackend"" which results in error when calling reload_hyperpipe-function in hyperpipe.py

Error:
.../photonai/base/json_transformer.py", line 72, in str_to_class
ValueError: Json Transformer is not able to initialize the hyperpipe. Class: FileSystemStoreBackend is not defined.

Perhaps a link to the Arxiv preprint at https://arxiv.org/abs/2002.05426 can be added to the readme of this repo.

The see .PHOTON format link is not working from the bottom of documentation page .

How should contributions be sent. I tried a push and received permission denied.

Regards, Bruce

While running a permutation test with Photon, the following issues occurred.

Issue 1:
While running the test (implemented as suggested in the docu https://www.photon-ai.com/documentation/permutation_test),
the script is unable to write the results of the very first permutation (y=ytrue) into the MongoDB since the file size is too large. Since the PermutationTest relies on the MonoDB entries the process finishes with code 1.

As a workaround, reducing the number of cv folds or the number of hyperparameter configurations (hence reducing the data to be stored in the MongoDB) solves the problem and the test will continue to run without any issues.
The most obvious solution to me would be to forgo saving certain data into the MongoDB (e.g. feature importances, predictions). To my understanding this had been implemented in the past but was discontinued (commit d5ecd1b, 24.09.2019).

Issue 2:
While calculating the permutation results, the server cannot be found. It seems as in the def _calculate_results function (line 181), the server is set to mongodb_path="mongodb://trap-umbriel:27017/photon_results" and not to the server which has been set by the user.

photonai version: 1.1.0 (develop tree)
OS: MAC OS 10.15.4
n samples: 1650
features (predictors): 55

Error log:
Issue 1:
File "/Users/michael/opt/anaconda3/envs/photon/lib/python3.7/site-packages/photonai/processing/permutation_test.py", line 90, in fit
self.pipe.results.save()
File "/Users/michael/opt/anaconda3/envs/photon/lib/python3.7/site-packages/pymodm/base/models.py", line 476, in save
self.to_son(), upsert=True)
File "/Users/michael/opt/anaconda3/envs/photon/lib/python3.7/site-packages/pymongo/collection.py", line 930, in replace_one
collation=collation, session=session),
File "/Users/michael/opt/anaconda3/envs/photon/lib/python3.7/site-packages/pymongo/collection.py", line 856, in _update_retryable
_update, session)
File "/Users/michael/opt/anaconda3/envs/photon/lib/python3.7/site-packages/pymongo/mongo_client.py", line 1491, in _retryable_write
return self._retry_with_session(retryable, func, s, None)
File "/Users/michael/opt/anaconda3/envs/photon/lib/python3.7/site-packages/pymongo/mongo_client.py", line 1384, in _retry_with_session
return func(session, sock_info, retryable)
File "/Users/michael/opt/anaconda3/envs/photon/lib/python3.7/site-packages/pymongo/collection.py", line 852, in _update
retryable_write=retryable_write)
File "/Users/michael/opt/anaconda3/envs/photon/lib/python3.7/site-packages/pymongo/collection.py", line 822, in _update
retryable_write=retryable_write).copy()
File "/Users/michael/opt/anaconda3/envs/photon/lib/python3.7/site-packages/pymongo/pool.py", line 618, in command
self._raise_connection_failure(error)
File "/Users/michael/opt/anaconda3/envs/photon/lib/python3.7/site-packages/pymongo/pool.py", line 613, in command
user_fields=user_fields)
File "/Users/michael/opt/anaconda3/envs/photon/lib/python3.7/site-packages/pymongo/network.py", line 143, in command
name, size, max_bson_size + message._COMMAND_OVERHEAD)
File "/Users/michael/opt/anaconda3/envs/photon/lib/python3.7/site-packages/pymongo/message.py", line 1077, in _raise_document_too_large
raise DocumentTooLarge("%r command document too large" % (operation,))
pymongo.errors.DocumentTooLarge: 'update' command document too large

Issue 2:
Traceback (most recent call last):
perm_tester.fit(X, y)
File "/Users/michael/opt/anaconda3/envs/photon/lib/python3.7/site-packages/photonai/processing/permutation_test.py", line 143, in fit
perm_result = self._calculate_results(self.permutation_id)
File "/Users/michael/opt/anaconda3/envs/photon/lib/python3.7/site-packages/photonai/processing/permutation_test.py", line 185, in _calculate_results
mother_permutation = PermutationTest.find_reference(mongodb_path, permutation_id)
File "/Users/michael/opt/anaconda3/envs/photon/lib/python3.7/site-packages/photonai/processing/permutation_test.py", line 291, in find_reference
mother_permutation = _find_mummy(permutation_id)
File "/Users/michael/opt/anaconda3/envs/photon/lib/python3.7/site-packages/photonai/processing/permutation_test.py", line 284, in _find_mummy
'computation_completed': True}).order_by([('computation_start_time', DESCENDING)]).first()
File "/Users/michael/opt/anaconda3/envs/photon/lib/python3.7/site-packages/pymodm/queryset.py", line 127, in first
return next(iter(self.limit(-1)))
File "/Users/michael/opt/anaconda3/envs/photon/lib/python3.7/site-packages/pymodm/queryset.py", line 543, in
return (to_instance(doc) for doc in self._get_raw_cursor())
File "/Users/michael/opt/anaconda3/envs/photon/lib/python3.7/site-packages/pymongo/cursor.py", line 1156, in next
if len(self.__data) or self._refresh():
File "/Users/michael/opt/anaconda3/envs/photon/lib/python3.7/site-packages/pymongo/cursor.py", line 1050, in _refresh
self.__session = self.__collection.database.client._ensure_session()
File "/Users/michael/opt/anaconda3/envs/photon/lib/python3.7/site-packages/pymongo/mongo_client.py", line 1810, in _ensure_session
return self.__start_session(True, causal_consistency=False)
File "/Users/michael/opt/anaconda3/envs/photon/lib/python3.7/site-packages/pymongo/mongo_client.py", line 1763, in __start_session
server_session = self._get_server_session()
File "/Users/michael/opt/anaconda3/envs/photon/lib/python3.7/site-packages/pymongo/mongo_client.py", line 1796, in _get_server_session
return self._topology.get_server_session()
File "/Users/michael/opt/anaconda3/envs/photon/lib/python3.7/site-packages/pymongo/topology.py", line 485, in get_server_session
None)
File "/Users/michael/opt/anaconda3/envs/photon/lib/python3.7/site-packages/pymongo/topology.py", line 209, in _select_servers_loop
self._error_message(selector))
pymongo.errors.ServerSelectionTimeoutError: trap-umbriel:27017: [Errno 8] nodename nor servname provided, or not known

Hi, and thanks for your work on PhotonAI !
I created an hyperpipe and added a PipelineElement about imbalanced data transformations, like explained on https://wwu-mmll.github.io/photonai/examples/imbalanced_data/ .
Unfortunately, when I look at my hyperpipe elements after the creation, I get the element PipelineElement(method_name='RandomUnderSampler', name='ImbalancedDataTransformer') for every selected method, event when selecting an oversampling method, which is quite embarrassing...
Do you have any idea about how to solve this issue, and effectively add the selected method as element to my hyperpipe ?
I am using version 2.1.0, maybe this issue has been addressed on version 2.2.0 ?
Thanks in advance for your help !
Clément

Hi, I’m a student and learning about BayesianOptimization rencently. I’m trying to make fabolas compactible to George 0.3.1 and I think I did it. And I hope I can give you some suggestions:

1. I suggest that using stationary kernel (E.g. SE kernel) instead of non-stationary kernel (LinearKernel in Fabolas.py), because when you run get_incumbent() (in Fabolas.py), you will project the environment variables to 1, and then change to 0 because of _quadratic_bf(). Then you will run predict() in get_incumbent(), and the parameters of predict() will be matrix with env=0 (E.g. (a1,b1,0) ,(a2,b2,0) ,(a3,b3,0)...)
   If you use LinearKernel, the var of predict() will be a zeroes, and mean will of predict() will be a vector with same elements.As a result, the epmgp.py cannot work

2. The parameter of EnvPrior() “n_lr=degree+1” can change to “n_lr= len(env_kernel) “

Evaluate if dataframe input instead of numpy array is feasable.